Welcome everyone, and thanks for joining my session on Transforming Knowledge Systems. I'm Haida Dube from Apex FinTech Solutions x Dell having a patent as well. And today we'll explore how embedding analytics within cloud native architectures, specifically Kubernetes can revolutionize knowledge management. This session is inspired by real enterprise challenges where static repository is no longer made dynamic data driven business needs. Let's start with the current state, or 50% of the enterprise knowledge management systems are failing to meet their performance goals. It's about 15,000 plus employees across the global offices. Many organizations are. Are struggling with knowledge accessibility. What's the key issue here? It is traditional systems were never designed for today's distributed cloud first environments. They are slow, siloed, and difficult to scale, hindering productivity and innovation. So what are the root causes of knowledge system failures? There are three. That. Those are the main ones. The siloed design where teams and departments operate in isolation, creating the fragmented knowledge repositories, outdated architecture that is monolith systems that cannot adapt quickly to changing business or user demands and poor adoption, where users abandon tools that fail to deliver relevant results or provide an intuitive interface. These issues make it clear that traditional knowledge management needs a complete architectural rethink. Now this slide contrast two paradigms, the static repository versus the containerized ecosystem. Traditional tools like SharePoint served as a static document, source and contrast. Kubernetes enables a dynamic adaptive ecosystem. A Kubernetes native approach supports automation, embedded analytics and continuous learning. Helping systems evolve with user needs. So what is the goal here? The goal is to move from infl inflexible systems to intelligent, responsive platforms. Here's an overview of the architecture where SharePoint Online serves as the content source, containerized services, managed analytics, search and automation. While Kubernetes orchestration ensures the scalability, resilience, and continuous deployment, this layered approach, as we call it, decoupled services, allowing independent scaling and modernization without disrupting the base content layer. There are three main components to it. User behavior analytics, which will track engagement, clickthroughs and navigation patterns, search pattern mining that identifies how users query and where searches fail, and real time feedback loops that adapt search results and recommendations based on user actions. Now this roadmap outlines how to evolve from setup to intelligent optimization during the foundation phase. Teams deploy Kubernetes clusters, design microservices, and integrate SharePoint APIs. During the analytics phase, you roll out analytics tracking and early AI recommendations. During the intelligence phase, introduce NLP and predictive analytics and the most important phase, and last but not the least, is optimization phase. That is when you automate governance and scale enterprise wide. After full implementation, which we saw were impressive, there were 33% faster information, retrieval rate, 35% fewer support tickets, and 22% boost in self-service resolution rates. These outcomes mean users find answers faster, support load decreases, and also knowledge flows freely. The key takeaways are embedded analytics makes systems smarter and self-improving over time. The impact extended to user experience as well. The metrics like user satisfaction was increased by 37.5%. The onboarding time dropped by almost 25%. Thing that was taking 14 weeks is now taking 11 weeks. And content relevance improved significantly. These gains comes from integrating feedback loops and relevance tracking proof that a better system directly enhances productivity and employee engagement. Smart features powered by Kubernetes. Kubernetes powers intelligence layer with NLS. That is natural language search by understanding intent beyond the keywords, context aware filtering where de delivering the results based on user rules or project context and predictive analytics By anticipating user needs before even the search, these capabilities turn a passive repository into an active assistant. Automation is not limited to content delivery. It extends to governance as well. User data, track engagement, abandonment metrics, detect friction points, training insights, identify popular or missing topics, and the continuous optimization automatically curates and promotes high value content in the loop. This governance loop ensures sustainability without manual intervention. The transformation delivered measurable, ROI, which is $3.7 million in tangible gains has come from efficiency improvement and using the metrics like we already covered in the previous slide. Here's how you can start. If you would like to have the similar blueprint in your organization first designed for Observa observability by instrumenting everything, embrace the microservices, modularize analytics components. Prioritize feedback by gathering real time user insights, automate governance, where you just let data manage content cycles and measure everything. Everything means everything. By defining and monitoring the success metrics. To summarize, evidence-based systems powered by Kubernetes and analytics enhance the engagement and agility. Transitioning from static to dynamic ecosystems unlocks the continuous improvement and real world implementation delivered both measurable ROI and Tulsa transformation, as you saw. So the key takeaway is Kubernetes when combined with embedded analytics, leads to resilience, scalable human-centered knowledge system. Thanks all for joining here, and I'm. I would love to hear your questions, whether about implementation, scaling analytics, or integrating these ideas in your own environment. Let's discuss and connect on LinkedIn how these concepts can fit your enterprise's unique challenges. I'd love your feedback. Thank you so much. Bye.